Metabolic syndrome and associated factors among psychiatric patients in Jimma University Specialized Hospital,South West Ethiopia

Background

Metabolic syndrome is a multisystem disorder which coined to describe the recognized clustering of metabolic and cardiovascular abnormalities including obesity, hypertension, dyslipidemia, and abnormalities of glucose homeostasis.

First quantitative analysis of cardiopulmonary resuscitation quality during in-hospital cardiac arrests of young children

Aim

The objective of this study is to report, for the first time, quantitative data on CPR quality during the resuscitation of children under 8 years of age. We hypothesized that the CPR performed would often not achieve 2010 Pediatric Basic Life Support (BLS) Guidelines, but would improve with the addition of audiovisual feedback.

Methods

Prospective observational cohort evaluating CPR quality during chest compression (CC) events in children between 1 and 8 years of age. CPR recording defibrillators collected CPR data (rate (CC/min), depth (mm), CC fraction (CCF), leaning (%> 2.5 kg.)). Audiovisual feedback was according to 2010 Guidelines in a subset of patients. The primary outcome, “excellent CPR” was defined as a CC rate ≥100 and ≤120 CC/min, depth ≥50 mm, CCF >0.80, and <20% of CC with leaning.

Results

8 CC events resulted in 285 thirty-second epochs of CPR (15,960 CCs). Percentage of epochs achieving targets was 54% (153/285) for rate, 19% (54/285) for depth, 88% (250/285) for CCF, 79% (226/285) for leaning, and 8% (24/285) for excellent CPR. The median percentage of epochs per event achieving targets increased with audiovisual feedback for rate [88 (IQR: 79, 94) vs. 39 (IQR 18, 62) %; p = 0.043] and excellent CPR [28 (IQR: 7.2, 52) vs. 0 (IQR: 0, 1) %; p = 0.018].

Conclusions

In-hospital pediatric CPR often does not meet 2010 Pediatric BLS Guidelines, but compliance is better when audiovisual feedback is provided to rescuers.     

Growth of pediatric intensive care units in the United States from 1995 to 2001

OBJECTIVE: To describe the growth and distribution of pediatric intensive care unit (PICU) beds in the United States from 1995 to 2001 and the characteristics of PICUs in 2001. STUDY DESIGN: This was a cross-sectional survey of PICUs in 1995 to 1996 and 2001 to 2002. RESULTS: There were 306 general PICUs in the United States in 1995 and 349 in 2001 (13.7% growth). In both survey periods, approximately half of the PICUs had or=15 beds. There were 3899 PICU beds in 2001 (23.9% increase from 1995), with a mean number of PICU beds per pediatric population (age <18 years) of 1/18542 in the United States (17.5% increase from 1995). There was an increase in the number of annual admissions, occupancy rate, length of stay, percentage intubated, mortality rate, and number of intensivists per PICU with increasing bed size. In 2001, 94% of PICUs had a pediatric intensivist on staff, and these specialists were in-house at night in 17% of all PICUs and in 30% of PICUs with >or=15 beds. CONCLUSIONS: The number of PICU beds is growing more rapidly than the rate of pediatric population growth. The impetus for this growth is unclear.     

Performance of stent thrombosis and bleeding risk scores in out-of-hospital cardiac arrest due to acute coronary syndromes

BackgroundPatients with out-of-hospital cardiac arrest (OHCA) due to acute coronary syndromes (ACS) who undergo percutaneous coronary intervention (PCI) are at high risk of bleeding and thrombosis. While predictive bleeding and stent thrombosis risk scores have been established, their performance in patients with OHCA has not been evaluated.MethodsAll consecutive patients admitted for OHCA due to ACS who underwent PCI between January 2007 and December 2019 were included. The ACTION and CRUSADE bleeding risk scores and the Dangas score for early stent thrombosis risk were calculated for each patient. A C-statistic analysis was performed to assess the performance of these scores.ResultsAmong 386 included patients, 82 patients (21.2%) experienced severe bleeding and 30 patients (7.8%) experienced stent thrombosis. The predictive performance of the ACTION and CRUSADE bleeding risk scores for major bleeding was poor, with areas under the curve (AUCs) of 0.596 and 0.548, respectively. Likewise, the predictive performance of the Dangas stent thrombosis risk score was poor (AUC 0.513). Using multivariable analysis, prolonged low-flow (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00–1.05; P = 0.025), reduced haematocrit or fibrinogen at admission (OR 0.93, 95% CI 0.88–0.98; P = 0.010 and OR 0.61; 95% CI 0.41–0.89; P = 0.012, respectively) and the use of glycoprotein IIb/IIIa inhibitors (OR 2.10, 95% CI 1.18–3.73; P = 0.011) were independent risk factors for major bleeding.ConclusionThe classic bleeding and stent thrombosis risk scores have poor performance in a population of patients with ACS complicated by OHCA. Other predictive factors might be more pertinent to determine major bleeding and stent thrombosis risks in this specific population.     

Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) DG Sanco Project No. 2013 12 04

To face the challenge of active and healthy ageing (AHA), European Health Systems and services should move towards proactive, anticipatory and integrated care. Health care systems thus need to personalize services, put patients at the centre of care and provide services using the adequate resources. Population health risk management is emphasized through the use of tools to stratify people with chronic diseases according to their risk. Effective screening of frailty is vital for optimizing the care of frail populations at risk. The Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) is an international effort whose aim is to bring together stratification-related professionals from Health Services, Academia and Research in the EU in order to (i) study current existing health risk stratification strategies and tools, (ii) spread their use and application on frail elderly patients, (iii) minimize deterioration of conditions and/or (iv) prevent emergency or hospital admissions. The analysis of Risk Stratification in different Health Systems will generate conclusions and risk stratification solutions, which will be transferable to a variety of regions in the future. ASSEHS is in line with Area 4 of the B3 Action Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA).     

Effect of influenza vaccine on markers of inflammation and lipid profile

Despite wide use of the influenza vaccine, relatively little is known about its effect on the measurement of inflammatory markers. Because inflammatory markers such as C-reactive protein (CRP) are increasingly being used in conjunction with lipids for the clinical assessment of cardiovascular disease and in epidemiologic studies, we evaluated the effect of influenza vaccination on markers of inflammation and plasma lipid concentrations. We drew blood from 22 healthy individuals 1 to 6 hours before they were given an influenza vaccination and 1, 3, and 7 days after the vaccination. Plasma CRP, interleukin (IL)-6, monocyte chemotactic protein 1, tumor necrosis factor alpha, IL-2 soluble receptor alpha, and serum amyloid A were measured, and differences in mean concentrations of absolute and normalized values on days 1, 3, and 7 were compared with mean baseline values. There was a significant increase in mean IL-6 (P < .01 absolute values, P < .001 normalized values) on day 1 after receiving the influenza vaccine. The mean increases in normalized high sensitivity CRP values were significant on day 1 (P < .01) and day 3 (P = .05), whereas the mean increase in normalized serum amyloid A was significant only on day 1 (P < .05). No significant changes were seen in mean concentrations of IL-2 soluble receptor alpha, monocyte chemotactic protein-1, or tumor necrosis factor-alpha. Of the lipids, significant decreases in mean concentrations of normalized triglyceride values were seen on days 1 (P < .05), 3 (P < .001), and 7 (P < .05) after vaccination. Our findings show that the influenza vaccination causes transient changes in select markers of inflammation and lipids. Consequently, clinical and epidemiologic interpretation of the biomarkers affected should take into account the possible effects of influenza vaccination.